Abstract P3-03-02: Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms
Authors: Dustin Valdez, Arianna Bunnell, Thomas Wolfgruber, Aleen Altamirano, Brandon Quon, Gertraud Maskarinec, Peter Sadowski, John Shepherd
Journal: Cancer Research (2023)

Abstract Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms Dustin Valdez12, Arianna Bunnell2, Thomas Wolfgruber1, Aleen V. Altamirano3, Brandon Quon1, Gertraud Maskarinec1, Peter Sadowski2, John A. Shepherd1 1 University of Hawaii Cancer Center, Honolulu, HI 2 University of Hawaii at Manoa, Honolulu, HI 3 Instituto Radiodiagnóstico, Managua, Nicaragua Background: Breast cancer is the second leading cause of cancer-related death among women in Hawaii and the Pacific. However, while there are programs like the Breast and Cervical Cancer Early Detection Program (BCCEDP) implemented throughout the Pacific, the lack of access to mammography screening and low screening participation rates contributes to very high advanced breast cancer rates in most cases over 50%. Portable breast ultrasound is a promising screening technology for low resource areas. However, without mammography, mammographic density is not available for risk modeling to determine who should participate in screening programs or at what frequency. In this study, we ask if breast ultrasound (US) images can be used to derive an equivalent mammographic density for risk modeling. We utilized artificial intelligence to derive breast density from diagnostic ultrasound images and compared to BI-RADS mammographic density in an established breast cancer risk model1. Methods: We selected women with negative screening visit who either later developed cancer (positives) or did not (negatives) over a 10-year period. Temporally-matched negative mammographic and ultrasound images, cancer outcome status and cancer risk information were sourced from the Hawaii and Pacific Islands Mammography Registry. US images had to have occurred within a year of the mammogram. BI-RADS mammographic density was derived using an existing deep neural network model2. Mammographic density was estimated from US images by training a deep-learning convolutional neural network model. A hold out set of images (Test set of 20% of the total) was used to compare 10-year breast cancer risk using the Tyrer-Cuzick (TC) risk model1 when calculated using breast density from either mammograms or US. The AUC values, confidence intervals, ROC plots and Pearson correlation were calculated and compared. Results: Over the 10-year study period, 1337 had matched mammograms and US images and 65 went on to develop breast cancer. Using the test set, the Pearson’s correlation between breast density from mammography and US was 0.31 (moderate correlation). There were no covariates found to improve this association. The AUC for TC 10-year personal risk was higher when breast density from mammograms was used 0.71 (95% CI=0.57-0.86) versus US images 0.65 (95% CI=0.53-0.76). Conclusion: Overall breast cancer risk was similar when breast density was derived from either mammograms or US. The performance of our US breast density model is expected to improve further when more US training data becomes available. Breast cancer screening programs exclusively using US imaging may be able to provide equivalent risk modeling to clinics using mammography. 1.Tyrer J, Duffy SW, Cuzick J (2004). A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004 Apr 15;23(7):1111-30. doi: 10.1002/sim.1668. Erratum in: Stat Med. 2005 Jan 15;24(1):156. PMID: 15057881. 2. Wu, N., K. J. Geras, Y. Shen, J. Su, S. G. Kim, E. Kim, S. Wolfson, L. Moy and K. Cho (2018). Breast Density Classification with Deep Convolutional Neural Networks. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. Citation Format: Dustin Valdez, Arianna Bunnell, Thomas Wolfgruber, Aleen Altamirano, Brandon Quon, Gertraud Maskarinec, Peter Sadowski, John Shepherd. Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-02.